ML Anthology

Topic Segmentation Algorithms for Text Summarization and Passage Retrieval: An Exhaustive Evaluation

Gaël Dias, Elsa Alves, José Gabriel Pereira Lopes
AAAI 2007 pp. 1334-1339
/aaai/2007/dias2007aaai-topic/

Abstract

In order to solve problems of reliability of systems based on lexical repetition and problems of adaptability of language-dependent systems, we present a context-based topic segmentation system based on a new informative similarity measure based on word co-occurrence. In particular, our evaluation with the state-of-the-art in the domain i.e. the c99 and the TextTiling algorithms shows improved results both with and without the identification of multiword units.

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Text

Dias et al. "Topic Segmentation Algorithms for Text Summarization and Passage Retrieval: An Exhaustive Evaluation." AAAI Conference on Artificial Intelligence, 2007.

Markdown

[Dias et al. "Topic Segmentation Algorithms for Text Summarization and Passage Retrieval: An Exhaustive Evaluation." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/dias2007aaai-topic/)

BibTeX

@inproceedings{dias2007aaai-topic,
  title     = {{Topic Segmentation Algorithms for Text Summarization and Passage Retrieval: An Exhaustive Evaluation}},
  author    = {Dias, Gaël and Alves, Elsa and Lopes, José Gabriel Pereira},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2007},
  pages     = {1334-1339},
  url       = {https://mlanthology.org/aaai/2007/dias2007aaai-topic/}
}